Group Members: Travis, Ira, Micah
Goal: This dashboard presents a multi-regional study of U.S. weather behavior in 2024.
It combines exploratory weather analysis, geographical mapping, and predictive modeling, including a classification machine-learning model.
Exploring wind data using the Meteostat Python API:
Source: Meteostat Python API
Time Period: 2024
Frame: Hourly and Daily
Key Variableswspd: Average wind speed (mph)wdir: Mean wind direction (degrees)temp: Temperature (°F)coco: Condition code| Midwest & Northeast Wind Analysis | ||
| 5-Year Hourly Averages (2020–2024) | ||
| Speed (km/h) | Direction (°) | |
|---|---|---|
| Midwest | ||
| Cleveland, OH | 18.0 | 226.0 |
| Chicago, IL | 15.9 | 255.0 |
| Detroit, MI | 15.8 | 249.0 |
| Milwaukee, WI | 15.4 | 286.0 |
| Minneapolis, MN | 12.6 | 311.0 |
| Northeast | ||
| Buffalo, NY | 17.9 | 243.0 |
| Boston, MA | 17.3 | 276.0 |
| Philadelphia, PA | 14.0 | 294.0 |
| Pittsburgh, PA | 11.5 | 284.0 |
| New York, NY | 11.0 | 294.0 |
| Southeast & West Wind Analysis | ||
| 5-Year Hourly Averages (2020–2024) | ||
| Speed (km/h) | Direction (°) | |
|---|---|---|
| Southeast | ||
| Jacksonville, FL | 13.0 | 16.0 |
| Miami, FL | 12.5 | 75.0 |
| Tampa, FL | 10.5 | 47.0 |
| Charlotte, NC | 10.2 | 326.0 |
| Atlanta, GA | 7.7 | 352.0 |
| West | ||
| San Francisco, CA | 13.4 | 295.0 |
| Denver, CO | 13.0 | 192.0 |
| Portland, OR | 10.8 | 330.0 |
| Seattle, WA | 8.3 | 306.0 |
| Los Angeles, CA | 6.6 | 330.0 |
click points on the map to see the wind speeds for the week surrounding a tornado (Shift+ click to select multiple tornados to compare).
the catagories are based on the Enhanced Fujita (EF) Scale: EF0 (65-85 mph), EF1 (86-110 mph), EF2 (111-135 mph), EF3 (136-165 mph), EF4 (166-200 mph), and EF5 (>200 mph). Our data visualizations does not seem exactly match with what we would expect to see. We Suspect the reasons for this may include the following: Tornados are categorized by peak wind speed rather than average wind speed; The wind speed is the wind speed at the closest weather station and the distance between stations and tornados can vary greatly; Tornados radius can vary greatly.
The Goal of this model is to predict wind vectors (speed and direction) at the following International Airports:
The Features used for prediction are the locations and wind vectors associated with the 5 closest stations for each airport.
We recorded the following Metrics on the test set:
| Metric | Value |
|---|---|
| R² (Coefficient of Determination) | 0.7408853538672449 |
| RMSE (Root Mean Squared Error) | 5.428108009372506 |
The \(R^2\) of \(\mathbf{0.74}\) suggests that approximately 74.1% of the variance in the true wind vectors is explained by the model. The RMSE of \(\mathbf{5.43}\) represents the average magnitude of the prediction error in \(\frac{km}{h}\).
R²: 0.7408853538672453
RMSE: 5.428108009372502Model Objective - This model predicts weather condition codes using an extensive batch of features.
Condition Code Groupings1. How do weather patterns change by region?
2. What are some case studies of extreme weather?
3. How do geographical features (lakes, oceans, mountains, deserts, plains) impact weather patterns?